We quantitatively analyze the effectiveness of our proposed audio-driven image manipulation approach. First, we measure performance on the semantic-level classification task. Given the audio embeddings from our pre-trained audio encoder, we train a linear classifier to recognize eight semantic labels, including giggling, sobbing, nose-blowing, fire crackling, wind noise, underwater bubbling, explosion, thunderstorm, gun, raining, and bird song. We use StyleGAN2 [1]} weights pre-trained from the FFHQ [2]} dataset for the guiding with giggling, sobbing, and nose-blowing attributes to compare the semantic-level classification accuracy between text and audio. Also, for the guiding with fire crackling, wind noise, underwater bubbling, explosion, and thunderstorm attributes, the weights of StyleGAN2 pre-trained on the LSUN (church) [3]} dataset are used. As shown in Fig. REF  (a), we generally outperform the existing text-driven manipulation approach with better semantically-rich latent representation.
